Forecasting Hurricanes using Large-Ensemble Output

This paper describes the development of a model framework for Forecasts of Hurricanes using Large-ensemble Outputs (FHLO). Computationally inexpensive, FHLO quantifies the forecast uncertainty of a particular tropical cyclone (TC) through O(1000) ensemble members. The model framework consists of three components: (1) a track model that generates synthetic tracks from the TC tracks of an ensemble numerical weather prediction (NWP) model, (2) an intensity model that predicts the intensity along each synthetic track, and (3) a TC wind field model that estimates the time-varying twodimensional surface wind field. In this framework, we consider the evolution of a TC’s intensity and wind field as though it were embedded in a timeevolving environmental field. The environmental fields are derived from the forecast fields of ensemble NWP models, leading to probabilistic forecasts of track, intensity, and wind speed that incorporate the flow-dependent uncertainty. Each component of the model is evaluated using four years (2015- 2018) of TC forecasts in the Atlantic and Eastern Pacific basins. We show that the synthetic track algorithm can generate tracks that are statistically similar to those of the underlying global ensemble models. We show that FHLO produces competitive intensity forecasts, especially when considering probabilistic verification statistics. We also demonstrate the reliability and accuracy of the probabilistic wind forecasts. Limitations of the model framework are also discussed.